Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations2275152
Missing cells801136
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory638.7 MiB
Average record size in memory294.4 B

Variable types

Numeric16
Categorical3

Alerts

canal is highly overall correlated with segmentoHigh correlation
densidad_hab is highly overall correlated with superficie_km2High correlation
dolar is highly overall correlated with id_periodo and 2 other fieldsHigh correlation
id_barrio is highly overall correlated with id_comunaHigh correlation
id_comuna is highly overall correlated with id_barrioHigh correlation
id_periodo is highly overall correlated with dolar and 2 other fieldsHigh correlation
segmento is highly overall correlated with canalHigh correlation
superficie_km2 is highly overall correlated with densidad_habHigh correlation
tpm is highly overall correlated with dolar and 2 other fieldsHigh correlation
uf is highly overall correlated with dolar and 2 other fieldsHigh correlation
descr_flag_patente is highly imbalanced (79.6%)Imbalance
indice_gse has 163039 (7.2%) missing valuesMissing
n_habitantes has 163039 (7.2%) missing valuesMissing
n_ptos_interes has 49565 (2.2%) missing valuesMissing
superficie_km2 has 49565 (2.2%) missing valuesMissing
densidad_hab has 163039 (7.2%) missing valuesMissing
uf has 28586 (1.3%) missing valuesMissing
dolar has 28586 (1.3%) missing valuesMissing
ipc has 28586 (1.3%) missing valuesMissing
imacec has 28586 (1.3%) missing valuesMissing
tpm has 28586 (1.3%) missing valuesMissing
tasa_desempleo has 55104 (2.4%) missing valuesMissing
liq_um is highly skewed (γ1 = 32.19111235)Skewed
ipc has 158878 (7.0%) zerosZeros
imacec has 81596 (3.6%) zerosZeros

Reproduction

Analysis started2025-10-18 19:14:44.770101
Analysis finished2025-10-18 19:16:50.097775
Duration2 minutes and 5.33 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

id_cliente
Real number (ℝ)

Distinct97065
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean374084.11
Minimum13
Maximum727517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 MiB
2025-10-18T16:16:50.126833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile58490
Q1188280
median375574
Q3560020
95-th percentile692591
Maximum727517
Range727504
Interquartile range (IQR)371740

Descriptive statistics

Standard deviation206905.61
Coefficient of variation (CV)0.55309918
Kurtosis-1.2225043
Mean374084.11
Median Absolute Deviation (MAD)185129
Skewness-0.082576884
Sum8.510982 × 1011
Variance4.2809933 × 1010
MonotonicityIncreasing
2025-10-18T16:16:50.184241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40308584
 
< 0.1%
40306384
 
< 0.1%
40302884
 
< 0.1%
40298184
 
< 0.1%
40318384
 
< 0.1%
54184
 
< 0.1%
22484
 
< 0.1%
22284
 
< 0.1%
47684
 
< 0.1%
40318084
 
< 0.1%
Other values (97055)2274312
> 99.9%
ValueCountFrequency (%)
139
 
< 0.1%
3344
< 0.1%
5156
< 0.1%
5645
< 0.1%
6230
< 0.1%
642
 
< 0.1%
8623
< 0.1%
12238
< 0.1%
14816
 
< 0.1%
1491
 
< 0.1%
ValueCountFrequency (%)
7275171
 
< 0.1%
7275161
 
< 0.1%
7275151
 
< 0.1%
72751310
 
< 0.1%
72751216
 
< 0.1%
7275101
 
< 0.1%
72750753
< 0.1%
72750552
< 0.1%
7274949
 
< 0.1%
7274937
 
< 0.1%

id_periodo
Real number (ℝ)

High correlation 

Distinct84
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202173.4
Minimum201809
Maximum202508
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 MiB
2025-10-18T16:16:50.242801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum201809
5-th percentile201812
Q1202005
median202203
Q3202311
95-th percentile202504
Maximum202508
Range699
Interquartile range (IQR)306

Descriptive statistics

Standard deviation205.34677
Coefficient of variation (CV)0.0010156963
Kurtosis-1.1244206
Mean202173.4
Median Absolute Deviation (MAD)196
Skewness-0.037009034
Sum4.5997521 × 1011
Variance42167.294
MonotonicityNot monotonic
2025-10-18T16:16:50.302097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20211234579
 
1.5%
20211032428
 
1.4%
20220132254
 
1.4%
20210932087
 
1.4%
20211131122
 
1.4%
20210830896
 
1.4%
20181229591
 
1.3%
20191229455
 
1.3%
20200129442
 
1.3%
20210629379
 
1.3%
Other values (74)1963919
86.3%
ValueCountFrequency (%)
20180928586
1.3%
20181028454
1.3%
20181128839
1.3%
20181229591
1.3%
20190129299
1.3%
20190229068
1.3%
20190328747
1.3%
20190428256
1.2%
20190528066
1.2%
20190627530
1.2%
ValueCountFrequency (%)
20250826410
1.2%
20250726324
1.2%
20250625814
1.1%
20250526316
1.2%
20250426518
1.2%
20250327056
1.2%
20250227222
1.2%
20250127807
1.2%
20241227950
1.2%
20241127400
1.2%

liq_um
Real number (ℝ)

Skewed 

Distinct303920
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.589309
Minimum-68.59356
Maximum8667.4965
Zeros18110
Zeros (%)0.8%
Negative811
Negative (%)< 0.1%
Memory size17.4 MiB
2025-10-18T16:16:50.360407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-68.59356
5-th percentile0.11088
Q10.93912
median3.63552
Q310.08588
95-th percentile35.73864
Maximum8667.4965
Range8736.09
Interquartile range (IQR)9.14676

Descriptive statistics

Standard deviation55.647904
Coefficient of variation (CV)4.8016587
Kurtosis1991.2608
Mean11.589309
Median Absolute Deviation (MAD)3.21552
Skewness32.191112
Sum26367438
Variance3096.6892
MonotonicityNot monotonic
2025-10-18T16:16:50.413982image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.058822980
 
1.0%
0.0554422261
 
1.0%
018110
 
0.8%
0.1108813715
 
0.6%
0.4212774
 
0.6%
0.0596412008
 
0.5%
0.16810246
 
0.5%
0.11769242
 
0.4%
0.219142
 
0.4%
0.0848884
 
0.4%
Other values (303910)2135790
93.9%
ValueCountFrequency (%)
-68.593561
< 0.1%
-52.762921
< 0.1%
-49.410481
< 0.1%
-47.225711
< 0.1%
-44.901361
< 0.1%
-44.731
< 0.1%
-41.05921
< 0.1%
-38.348521
< 0.1%
-36.331
< 0.1%
-34.033441
< 0.1%
ValueCountFrequency (%)
8667.496461
< 0.1%
8156.205961
< 0.1%
7356.056121
< 0.1%
7270.730881
< 0.1%
7113.102081
< 0.1%
6430.843721
< 0.1%
6206.291841
< 0.1%
5456.77441
< 0.1%
5078.53921
< 0.1%
4295.259641
< 0.1%

id_barrio
Real number (ℝ)

High correlation 

Distinct1981
Distinct (%)0.1%
Missing14855
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean897229.75
Minimum110110
Maximum1510511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 MiB
2025-10-18T16:16:50.464459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum110110
5-th percentile230113
Q1610813
median840311
Q31310912
95-th percentile1360111
Maximum1510511
Range1400401
Interquartile range (IQR)700099

Descriptive statistics

Standard deviation361334.33
Coefficient of variation (CV)0.40272219
Kurtosis-1.0146903
Mean897229.75
Median Absolute Deviation (MAD)310199
Skewness-0.16255455
Sum2.0280057 × 1012
Variance1.305625 × 1011
MonotonicityNot monotonic
2025-10-18T16:16:50.519858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131012413817
 
0.6%
10201109440
 
0.4%
6201109025
 
0.4%
5103108480
 
0.4%
8102137420
 
0.3%
8401247420
 
0.3%
10109157245
 
0.3%
13124197159
 
0.3%
2101147053
 
0.3%
10101216906
 
0.3%
Other values (1971)2176332
95.7%
(Missing)14855
 
0.7%
ValueCountFrequency (%)
1101105194
0.2%
1101111776
 
0.1%
1101122445
0.1%
1101132867
0.1%
1101142605
0.1%
1101151464
 
0.1%
1101162241
0.1%
1101172840
0.1%
1101183313
0.1%
110119406
 
< 0.1%
ValueCountFrequency (%)
15105111386
0.1%
15101285
 
< 0.1%
15101261084
 
< 0.1%
1510125655
 
< 0.1%
15101243458
0.2%
15101231635
0.1%
15101222572
0.1%
1510121772
 
< 0.1%
15101201576
0.1%
1510119869
 
< 0.1%

id_comuna
Real number (ℝ)

High correlation 

Distinct326
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8976.8386
Minimum1101
Maximum15105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 MiB
2025-10-18T16:16:50.574212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1101
5-th percentile2301
Q16108
median8403
Q313109
95-th percentile13601
Maximum15105
Range14004
Interquartile range (IQR)7001

Descriptive statistics

Standard deviation3615.3993
Coefficient of variation (CV)0.4027475
Kurtosis-1.0144819
Mean8976.8386
Median Absolute Deviation (MAD)3102
Skewness-0.16688969
Sum2.0423672 × 1010
Variance13071112
MonotonicityNot monotonic
2025-10-18T16:16:50.628534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1310167698
 
3.0%
510146191
 
2.0%
510944024
 
1.9%
810141815
 
1.8%
210140435
 
1.8%
840139283
 
1.7%
910136402
 
1.6%
1010133006
 
1.5%
1312332759
 
1.4%
710132278
 
1.4%
Other values (316)1861261
81.8%
ValueCountFrequency (%)
110128863
1.3%
110710611
 
0.5%
14011891
 
0.1%
210140435
1.8%
21022340
 
0.1%
2103708
 
< 0.1%
21043179
 
0.1%
220117523
0.8%
22033916
 
0.2%
23016293
 
0.3%
ValueCountFrequency (%)
151051386
 
0.1%
1510126005
1.1%
142045453
 
0.2%
142032197
 
0.1%
142023367
 
0.1%
142015588
 
0.2%
141087059
 
0.3%
141073304
 
0.1%
141065978
 
0.3%
141042504
 
0.1%

segmento
Categorical

High correlation 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size110.7 MiB
AL
814043 
BO
616232 
RT
378188 
FS
223117 
BA
81936 
Other values (14)
161636 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters4550304
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowMA
2nd rowMA
3rd rowMA
4th rowMA
5th rowMA

Common Values

ValueCountFrequency (%)
AL814043
35.8%
BO616232
27.1%
RT378188
16.6%
FS223117
 
9.8%
BA81936
 
3.6%
MA68991
 
3.0%
IE60902
 
2.7%
AP13512
 
0.6%
FF10803
 
0.5%
DI6151
 
0.3%
Other values (9)1277
 
0.1%

Length

2025-10-18T16:16:50.833764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
al814043
35.8%
bo616232
27.1%
rt378188
16.6%
fs223117
 
9.8%
ba81936
 
3.6%
ma68991
 
3.0%
ie60902
 
2.7%
ap13512
 
0.6%
ff10803
 
0.5%
di6151
 
0.3%
Other values (9)1277
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A978485
21.5%
L814043
17.9%
B698169
15.3%
O616232
13.5%
R379094
 
8.3%
T378188
 
8.3%
F244737
 
5.4%
S223131
 
4.9%
M68992
 
1.5%
I67376
 
1.5%
Other values (5)81857
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4550304
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A978485
21.5%
L814043
17.9%
B698169
15.3%
O616232
13.5%
R379094
 
8.3%
T378188
 
8.3%
F244737
 
5.4%
S223131
 
4.9%
M68992
 
1.5%
I67376
 
1.5%
Other values (5)81857
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4550304
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A978485
21.5%
L814043
17.9%
B698169
15.3%
O616232
13.5%
R379094
 
8.3%
T378188
 
8.3%
F244737
 
5.4%
S223131
 
4.9%
M68992
 
1.5%
I67376
 
1.5%
Other values (5)81857
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4550304
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A978485
21.5%
L814043
17.9%
B698169
15.3%
O616232
13.5%
R379094
 
8.3%
T378188
 
8.3%
F244737
 
5.4%
S223131
 
4.9%
M68992
 
1.5%
I67376
 
1.5%
Other values (5)81857
 
1.8%

canal
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.3 MiB
COMPRA
1430278 
CONSUMO
775882 
MAYORISTA
 
68992

Length

Max length9
Median length6
Mean length6.4319966
Min length6

Characters and Unicode

Total characters14633770
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMAYORISTA
2nd rowMAYORISTA
3rd rowMAYORISTA
4th rowMAYORISTA
5th rowMAYORISTA

Common Values

ValueCountFrequency (%)
COMPRA1430278
62.9%
CONSUMO775882
34.1%
MAYORISTA68992
 
3.0%

Length

2025-10-18T16:16:50.876558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-18T16:16:50.910156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
compra1430278
62.9%
consumo775882
34.1%
mayorista68992
 
3.0%

Most occurring characters

ValueCountFrequency (%)
O3051034
20.8%
M2275152
15.5%
C2206160
15.1%
A1568262
10.7%
R1499270
10.2%
P1430278
9.8%
S844874
 
5.8%
N775882
 
5.3%
U775882
 
5.3%
Y68992
 
0.5%
Other values (2)137984
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)14633770
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O3051034
20.8%
M2275152
15.5%
C2206160
15.1%
A1568262
10.7%
R1499270
10.2%
P1430278
9.8%
S844874
 
5.8%
N775882
 
5.3%
U775882
 
5.3%
Y68992
 
0.5%
Other values (2)137984
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)14633770
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O3051034
20.8%
M2275152
15.5%
C2206160
15.1%
A1568262
10.7%
R1499270
10.2%
P1430278
9.8%
S844874
 
5.8%
N775882
 
5.3%
U775882
 
5.3%
Y68992
 
0.5%
Other values (2)137984
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)14633770
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O3051034
20.8%
M2275152
15.5%
C2206160
15.1%
A1568262
10.7%
R1499270
10.2%
P1430278
9.8%
S844874
 
5.8%
N775882
 
5.3%
U775882
 
5.3%
Y68992
 
0.5%
Other values (2)137984
 
0.9%

descr_flag_patente
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size130.1 MiB
CON PATENTE
2111529 
SIN PATENTE
 
148113
NaN
 
14855
None
 
655

Length

Max length11
Median length11
Mean length10.945751
Min length3

Characters and Unicode

Total characters24903247
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCON PATENTE
2nd rowCON PATENTE
3rd rowCON PATENTE
4th rowCON PATENTE
5th rowCON PATENTE

Common Values

ValueCountFrequency (%)
CON PATENTE2111529
92.8%
SIN PATENTE148113
 
6.5%
NaN14855
 
0.7%
None655
 
< 0.1%

Length

2025-10-18T16:16:50.951494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-18T16:16:50.982113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
patente2259642
49.8%
con2111529
46.6%
sin148113
 
3.3%
nan14855
 
0.3%
none655
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N4549649
18.3%
T4519284
18.1%
E4519284
18.1%
A2259642
9.1%
P2259642
9.1%
2259642
9.1%
C2111529
8.5%
O2111529
8.5%
S148113
 
0.6%
I148113
 
0.6%
Other values (4)16820
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)24903247
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N4549649
18.3%
T4519284
18.1%
E4519284
18.1%
A2259642
9.1%
P2259642
9.1%
2259642
9.1%
C2111529
8.5%
O2111529
8.5%
S148113
 
0.6%
I148113
 
0.6%
Other values (4)16820
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)24903247
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N4549649
18.3%
T4519284
18.1%
E4519284
18.1%
A2259642
9.1%
P2259642
9.1%
2259642
9.1%
C2111529
8.5%
O2111529
8.5%
S148113
 
0.6%
I148113
 
0.6%
Other values (4)16820
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)24903247
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N4549649
18.3%
T4519284
18.1%
E4519284
18.1%
A2259642
9.1%
P2259642
9.1%
2259642
9.1%
C2111529
8.5%
O2111529
8.5%
S148113
 
0.6%
I148113
 
0.6%
Other values (4)16820
 
0.1%

indice_gse
Real number (ℝ)

Missing 

Distinct1357
Distinct (%)0.1%
Missing163039
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean0.1099656
Minimum0
Maximum0.61673068
Zeros50
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.4 MiB
2025-10-18T16:16:51.029640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.040179376
Q10.067208815
median0.087894807
Q30.13044712
95-th percentile0.23206455
Maximum0.61673068
Range0.61673068
Interquartile range (IQR)0.063238303

Descriptive statistics

Standard deviation0.075519569
Coefficient of variation (CV)0.68675629
Kurtosis8.8404871
Mean0.1099656
Median Absolute Deviation (MAD)0.02636588
Skewness2.6311127
Sum232259.78
Variance0.0057032053
MonotonicityNot monotonic
2025-10-18T16:16:51.087238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.199085232313817
 
0.6%
0.073296729929440
 
0.4%
0.086857389999025
 
0.4%
0.18968504168480
 
0.4%
0.077852064827420
 
0.3%
0.085260952117420
 
0.3%
0.13358915057245
 
0.3%
0.095413439677159
 
0.3%
0.17371725177053
 
0.3%
0.087306383376906
 
0.3%
Other values (1347)2028148
89.1%
(Missing)163039
 
7.2%
ValueCountFrequency (%)
050
 
< 0.1%
0.016333799861239
0.1%
0.017426284751464
0.1%
0.01762380952484
 
< 0.1%
0.017650707291168
0.1%
0.018420438961679
0.1%
0.022406655842580
0.1%
0.023091872791259
0.1%
0.02337123746576
 
< 0.1%
0.0239652968688
 
< 0.1%
ValueCountFrequency (%)
0.6167306786580
 
< 0.1%
0.5621148523205
 
< 0.1%
0.54046443092561
0.1%
0.53396639472429
0.1%
0.5241336952951
 
< 0.1%
0.5131611595507
 
< 0.1%
0.50816055051392
 
0.1%
0.49198684724635
0.2%
0.47266911733519
0.2%
0.4457646212562
 
< 0.1%

n_habitantes
Real number (ℝ)

Missing 

Distinct1312
Distinct (%)0.1%
Missing163039
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean16027.083
Minimum0
Maximum99870
Zeros50
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.4 MiB
2025-10-18T16:16:51.145570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1700
Q16554
median12880
Q320954
95-th percentile44099
Maximum99870
Range99870
Interquartile range (IQR)14400

Descriptive statistics

Standard deviation13939.504
Coefficient of variation (CV)0.8697468
Kurtosis7.4084184
Mean16027.083
Median Absolute Deviation (MAD)7032
Skewness2.1720275
Sum3.385101 × 1010
Variance1.9430977 × 108
MonotonicityNot monotonic
2025-10-18T16:16:51.200538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2095413817
 
0.6%
253639440
 
0.4%
127769025
 
0.4%
391758480
 
0.4%
14557554
 
0.3%
507767420
 
0.3%
550317420
 
0.3%
249107245
 
0.3%
970447159
 
0.3%
98617053
 
0.3%
Other values (1302)2027500
89.1%
(Missing)163039
 
7.2%
ValueCountFrequency (%)
050
 
< 0.1%
713
 
< 0.1%
1251
 
< 0.1%
14146
 
< 0.1%
23123
 
< 0.1%
31490
< 0.1%
32828
< 0.1%
34160
 
< 0.1%
46971
< 0.1%
6188
 
< 0.1%
ValueCountFrequency (%)
998703839
0.2%
970447159
0.3%
797551184
 
0.1%
791244463
0.2%
739583181
0.1%
728881412
 
0.1%
616542795
 
0.1%
603323519
0.2%
602601289
 
0.1%
580806906
0.3%

n_ptos_interes
Real number (ℝ)

Missing 

Distinct173
Distinct (%)< 0.1%
Missing49565
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean61.331289
Minimum1
Maximum596
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 MiB
2025-10-18T16:16:51.256227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median31
Q375
95-th percentile220
Maximum596
Range595
Interquartile range (IQR)62

Descriptive statistics

Standard deviation85.984155
Coefficient of variation (CV)1.4019623
Kurtosis13.552632
Mean61.331289
Median Absolute Deviation (MAD)22
Skewness3.2660878
Sum1.3649812 × 108
Variance7393.275
MonotonicityNot monotonic
2025-10-18T16:16:51.310171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
552224
 
2.3%
948377
 
2.1%
1648373
 
2.1%
248080
 
2.1%
145737
 
2.0%
645033
 
2.0%
744830
 
2.0%
1244705
 
2.0%
444060
 
1.9%
344016
 
1.9%
Other values (163)1760152
77.4%
(Missing)49565
 
2.2%
ValueCountFrequency (%)
145737
2.0%
248080
2.1%
344016
1.9%
444060
1.9%
552224
2.3%
645033
2.0%
744830
2.0%
842827
1.9%
948377
2.1%
1030121
1.3%
ValueCountFrequency (%)
59613817
0.6%
5393282
 
0.1%
5013519
 
0.2%
4617245
0.3%
4465223
 
0.2%
3655905
0.3%
3613300
 
0.1%
3596652
0.3%
3435041
 
0.2%
2904636
 
0.2%

superficie_km2
Real number (ℝ)

High correlation  Missing 

Distinct1808
Distinct (%)0.1%
Missing49565
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean89.221837
Minimum0.10992095
Maximum17132.919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 MiB
2025-10-18T16:16:51.364500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.10992095
5-th percentile0.73761558
Q11.6548132
median9.1290359
Q349.179277
95-th percentile277.40182
Maximum17132.919
Range17132.809
Interquartile range (IQR)47.524464

Descriptive statistics

Standard deviation379.59622
Coefficient of variation (CV)4.2545214
Kurtosis264.42596
Mean89.221837
Median Absolute Deviation (MAD)8.2322934
Skewness12.220727
Sum1.9857096 × 108
Variance144093.29
MonotonicityNot monotonic
2025-10-18T16:16:51.420536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.21264882513817
 
0.6%
8.6174780039440
 
0.4%
142.08383969025
 
0.4%
11.869726518480
 
0.4%
64.862807077420
 
0.3%
26.653558947420
 
0.3%
117.82131957245
 
0.3%
22.981896227159
 
0.3%
1.6537356017053
 
0.3%
24.245936126906
 
0.3%
Other values (1798)2141622
94.1%
(Missing)49565
 
2.2%
ValueCountFrequency (%)
0.10992095332301
0.1%
0.150668958427
 
< 0.1%
0.2286851919233
 
< 0.1%
0.2662815307786
 
< 0.1%
0.2857682808533
 
< 0.1%
0.3062273498555
 
< 0.1%
0.31849379313787
0.2%
0.3213928872791
 
< 0.1%
0.324933443512
 
< 0.1%
0.32528755312956
0.1%
ValueCountFrequency (%)
17132.9189361
 
< 0.1%
10403.7113408
 
< 0.1%
9104.6506892
 
< 0.1%
8827.6944914
 
< 0.1%
6473.26707434
 
< 0.1%
5013.033193112
 
< 0.1%
4804.26701448
 
< 0.1%
4753.3807433
 
< 0.1%
4475.328644879
< 0.1%
4371.3584491247
0.1%

densidad_hab
Real number (ℝ)

High correlation  Missing 

Distinct1357
Distinct (%)0.1%
Missing163039
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean4762.4958
Minimum0
Maximum45213.161
Zeros50
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size17.4 MiB
2025-10-18T16:16:51.476428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.214627
Q1239.89805
median2487.9619
Q37698.696
95-th percentile15231.449
Maximum45213.161
Range45213.161
Interquartile range (IQR)7458.798

Descriptive statistics

Standard deviation5929.2346
Coefficient of variation (CV)1.2449847
Kurtosis8.9066212
Mean4762.4958
Median Absolute Deviation (MAD)2420.8443
Skewness2.2501231
Sum1.0058929 × 1010
Variance35155823
MonotonicityNot monotonic
2025-10-18T16:16:51.529779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17279.5285613817
 
0.6%
2943.2044969440
 
0.4%
89.918741199025
 
0.4%
3300.4130268480
 
0.4%
848.4214997420
 
0.3%
1905.0364017420
 
0.3%
211.42183877245
 
0.3%
4222.6280667159
 
0.3%
5962.8637117053
 
0.3%
2395.4529836906
 
0.3%
Other values (1347)2028148
89.1%
(Missing)163039
 
7.2%
ValueCountFrequency (%)
050
 
< 0.1%
0.01553075957123
 
< 0.1%
0.0566525392651
 
< 0.1%
0.058466510315
 
< 0.1%
0.0819880426913
 
< 0.1%
0.1840793419146
 
< 0.1%
0.189177209584
 
< 0.1%
0.3240110898288
 
< 0.1%
0.3983416751287
0.1%
0.5429295912949
< 0.1%
ValueCountFrequency (%)
45213.160773439
0.2%
43586.474533134
0.1%
42636.912652673
0.1%
37937.90242791
0.1%
29464.328133399
0.1%
25148.026931795
0.1%
22474.679341110
 
< 0.1%
22011.04649772
 
< 0.1%
21551.837951095
 
< 0.1%
21421.878341656
0.1%

uf
Real number (ℝ)

High correlation  Missing 

Distinct83
Distinct (%)< 0.1%
Missing28586
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean32723.977
Minimum27432.1
Maximum39383.07
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 MiB
2025-10-18T16:16:51.582245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum27432.1
5-th percentile27556.9
Q128690.73
median31727.74
Q336733.04
95-th percentile39075.41
Maximum39383.07
Range11950.97
Interquartile range (IQR)8042.31

Descriptive statistics

Standard deviation4114.7451
Coefficient of variation (CV)0.12574099
Kurtosis-1.5632775
Mean32723.977
Median Absolute Deviation (MAD)3774.32
Skewness0.1722175
Sum7.3516574 × 1010
Variance16931127
MonotonicityNot monotonic
2025-10-18T16:16:51.638716image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30991.7434579
 
1.5%
30380.5332428
 
1.4%
31212.6532254
 
1.4%
30088.3732087
 
1.4%
30762.831122
 
1.4%
29935.0830896
 
1.4%
27565.7929591
 
1.3%
28309.9429455
 
1.3%
28338.2529442
 
1.3%
29709.8329379
 
1.3%
Other values (73)1935333
85.1%
ValueCountFrequency (%)
27432.128454
1.3%
27532.828839
1.3%
27546.2229299
1.3%
27556.929068
1.3%
27565.7628747
1.3%
27565.7929591
1.3%
27662.1728256
1.2%
27762.5528066
1.2%
27903.327530
1.2%
27953.4227841
1.2%
ValueCountFrequency (%)
39383.0726410
1.2%
39267.0725814
1.1%
39189.4526316
1.2%
39179.0126324
1.2%
39075.4126518
1.2%
38894.1127056
1.2%
38647.9427222
1.2%
38416.6927950
1.2%
38384.4127807
1.2%
38247.9227400
1.2%

dolar
Real number (ℝ)

High correlation  Missing 

Distinct83
Distinct (%)< 0.1%
Missing28586
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean829.48442
Minimum649.92
Maximum992.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 MiB
2025-10-18T16:16:51.691633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum649.92
5-th percentile677.67
Q1744.62
median816.36
Q3917.98
95-th percentile978.07
Maximum992.12
Range342.2
Interquartile range (IQR)173.36

Descriptive statistics

Standard deviation96.598905
Coefficient of variation (CV)0.11645656
Kurtosis-1.1525888
Mean829.48442
Median Absolute Deviation (MAD)90.02
Skewness-0.0048642029
Sum1.8634915 × 109
Variance9331.3484
MonotonicityNot monotonic
2025-10-18T16:16:51.749008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
850.2534579
 
1.5%
805.4732428
 
1.4%
810.1232254
 
1.4%
803.5932087
 
1.4%
836.7331122
 
1.4%
779.9730896
 
1.4%
695.6929591
 
1.3%
744.6229455
 
1.3%
797.9629442
 
1.3%
735.2829379
 
1.3%
Other values (73)1935333
85.1%
ValueCountFrequency (%)
649.9229068
1.3%
666.7629299
1.3%
669.4328839
1.3%
677.6728256
1.2%
679.8627530
1.2%
681.0928747
1.3%
693.3128454
1.3%
695.6929591
1.3%
699.9827841
1.2%
705.0923649
1.0%
ValueCountFrequency (%)
992.1227950
1.2%
988.127807
1.2%
982.3827420
1.2%
980.1927665
1.2%
978.0726324
1.2%
977.3227400
1.2%
967.4826410
1.2%
96627843
1.2%
956.5826584
1.2%
951.2127222
1.2%

ipc
Real number (ℝ)

Missing  Zeros 

Distinct20
Distinct (%)< 0.1%
Missing28586
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean0.45368238
Minimum-0.5
Maximum1.9
Zeros158878
Zeros (%)7.0%
Negative252560
Negative (%)11.1%
Memory size17.4 MiB
2025-10-18T16:16:51.796204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-0.5
5-th percentile-0.1
Q10.1
median0.4
Q30.7
95-th percentile1.3
Maximum1.9
Range2.4
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.456836
Coefficient of variation (CV)1.0069512
Kurtosis0.16736304
Mean0.45368238
Median Absolute Deviation (MAD)0.3
Skewness0.59990615
Sum1019227.4
Variance0.20869913
MonotonicityNot monotonic
2025-10-18T16:16:51.838015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.4246109
10.8%
0.3243842
10.7%
0.1235804
10.4%
0.2161376
 
7.1%
0158878
 
7.0%
0.7156100
 
6.9%
-0.1144000
 
6.3%
0.5141528
 
6.2%
0.8119581
 
5.3%
1.2118784
 
5.2%
Other values (10)520564
22.9%
ValueCountFrequency (%)
-0.528144
 
1.2%
-0.425814
 
1.1%
-0.254602
 
2.4%
-0.1144000
6.3%
0158878
7.0%
0.1235804
10.4%
0.2161376
7.1%
0.3243842
10.7%
0.4246109
10.8%
0.5141528
6.2%
ValueCountFrequency (%)
1.927693
 
1.2%
1.454071
 
2.4%
1.332428
 
1.4%
1.2118784
5.2%
1.155818
 
2.5%
154837
 
2.4%
0.980835
3.6%
0.8119581
5.3%
0.7156100
6.9%
0.6106322
4.7%

imacec
Real number (ℝ)

Missing  Zeros 

Distinct63
Distinct (%)< 0.1%
Missing28586
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean2.627763
Minimum-15.3
Maximum20.1
Zeros81596
Zeros (%)3.6%
Negative627580
Negative (%)27.6%
Memory size17.4 MiB
2025-10-18T16:16:51.885223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-15.3
5-th percentile-5.3
Q1-0.4
median1.9
Q33.8
95-th percentile18.1
Maximum20.1
Range35.4
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation6.3186971
Coefficient of variation (CV)2.4045917
Kurtosis1.7199985
Mean2.627763
Median Absolute Deviation (MAD)2.3
Skewness0.58892429
Sum5903443
Variance39.925933
MonotonicityNot monotonic
2025-10-18T16:16:51.941574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-183592
 
3.7%
2.582357
 
3.6%
081596
 
3.6%
2.381575
 
3.6%
1.155888
 
2.5%
2.155656
 
2.4%
4.255038
 
2.4%
3.754806
 
2.4%
3.154653
 
2.4%
3.254157
 
2.4%
Other values (53)1587248
69.8%
ValueCountFrequency (%)
-15.318472
0.8%
-14.118618
0.8%
-12.417852
0.8%
-11.319728
0.9%
-10.718213
0.8%
-5.321149
0.9%
-3.527527
1.2%
-3.428211
1.2%
-3.328505
1.3%
-3.125002
1.1%
ValueCountFrequency (%)
20.129379
1.3%
19.130896
1.4%
18.154083
2.4%
15.632087
1.4%
1532428
1.4%
14.331122
1.4%
14.123649
1.0%
10.134579
1.5%
932254
1.4%
7.227693
1.2%

tpm
Real number (ℝ)

High correlation  Missing 

Distinct26
Distinct (%)< 0.1%
Missing28586
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean4.9792035
Minimum0.5
Maximum11.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 MiB
2025-10-18T16:16:51.993872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.5
Q11.75
median5
Q38.25
95-th percentile11.25
Maximum11.25
Range10.75
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation3.6063281
Coefficient of variation (CV)0.72427812
Kurtosis-1.0915321
Mean4.9792035
Median Absolute Deviation (MAD)3.25
Skewness0.42498268
Sum11186109
Variance13.005602
MonotonicityNot monotonic
2025-10-18T16:16:52.040592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.5339518
14.9%
11.25249324
 
11.0%
5188683
 
8.3%
2.75150434
 
6.6%
1.75144914
 
6.4%
3143436
 
6.3%
5.587042
 
3.8%
2.583509
 
3.7%
8.2583332
 
3.7%
981134
 
3.6%
Other values (16)695240
30.6%
ValueCountFrequency (%)
0.5339518
14.9%
0.7559462
 
2.6%
127527
 
1.2%
1.532087
 
1.4%
1.75144914
6.4%
228413
 
1.2%
2.583509
 
3.7%
2.75150434
6.6%
3143436
6.3%
434579
 
1.5%
ValueCountFrequency (%)
11.25249324
11.0%
10.7527843
 
1.2%
10.2554014
 
2.4%
9.7554044
 
2.4%
9.527281
 
1.2%
981134
 
3.6%
8.2583332
 
3.7%
7.2555085
 
2.4%
755007
 
2.4%
6.527058
 
1.2%

tasa_desempleo
Real number (ℝ)

Missing 

Distinct70
Distinct (%)< 0.1%
Missing55104
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean8.4171702
Minimum6.7
Maximum13.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.4 MiB
2025-10-18T16:16:52.088801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.7
5-th percentile6.8
Q17.53
median8.35
Q38.81
95-th percentile11.21
Maximum13.09
Range6.39
Interquartile range (IQR)1.28

Descriptive statistics

Standard deviation1.2934765
Coefficient of variation (CV)0.15367118
Kurtosis2.4602676
Mean8.4171702
Median Absolute Deviation (MAD)0.55
Skewness1.3982367
Sum18686522
Variance1.6730815
MonotonicityNot monotonic
2025-10-18T16:16:52.140926image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.1962717
 
2.8%
8.0860378
 
2.7%
6.758659
 
2.6%
6.858138
 
2.6%
8.0456068
 
2.5%
7.155984
 
2.5%
7.8155969
 
2.5%
7.854849
 
2.4%
8.5254099
 
2.4%
8.6854004
 
2.4%
Other values (60)1649183
72.5%
(Missing)55104
 
2.4%
ValueCountFrequency (%)
6.758659
2.6%
6.858138
2.6%
6.8528747
1.3%
6.8628256
1.2%
6.8828505
1.3%
6.9829455
1.3%
7.0128211
1.2%
7.0528413
1.2%
7.0928066
1.2%
7.155984
2.5%
ValueCountFrequency (%)
13.0918213
0.8%
12.9319728
0.9%
12.3521149
0.9%
12.2517852
0.8%
11.5821946
1.0%
11.2118472
0.8%
10.7624002
1.1%
10.3525098
1.1%
10.325163
1.1%
10.2925730
1.1%

Interactions

2025-10-18T16:16:40.527752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:32.186303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:36.808867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:41.723967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:46.222158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:50.972780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:55.493754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:59.890839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:04.292016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:08.955731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:13.537532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:17.833201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:22.331706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:26.913007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:31.404286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:36.077768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:40.817747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:32.463799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:37.084455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:42.014277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:46.514236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:51.258898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:55.769500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:00.174626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:04.587252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:09.258562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:13.810908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:18.125293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:22.634108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:27.204288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:31.695547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:36.373095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:41.108310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:32.743449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:37.382194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:42.287095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:46.800789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:51.537676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:56.050828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:00.449846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:04.878524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:09.550268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:14.081821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:18.412995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:22.925846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:27.489164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:31.988604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:36.665128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:41.389019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:33.038285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:37.695750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:42.564444image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:47.083759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:51.819583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:56.333574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:00.718379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:05.157362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:09.840103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:14.358058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:18.695458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:23.221147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:27.771841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:32.276586image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:36.942359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:41.682242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:33.319856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:37.993654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:42.836733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:47.384349image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:52.093217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:56.619298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:00.987147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:05.451184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:10.122327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:14.650938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:18.977644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:23.513485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:28.059256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:32.571081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:37.227391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:41.954040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:33.603201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:38.301464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:43.109173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:47.665608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:52.364059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:56.891147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:01.268415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:05.722825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:10.389403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:14.914268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:19.241892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:23.785777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:28.325963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:32.845221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:37.490533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:42.232657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:33.884573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:38.614270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:43.375881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:48.128584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:52.634579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:57.169770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:01.537338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:05.995080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:10.652562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:15.181075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:19.512507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:24.063648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:28.598013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:33.118880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:37.754244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:42.536164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:34.177431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:38.924115image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:43.654331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:48.410340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:52.919627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:57.449505image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:01.838933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:06.263556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:11.069042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:15.445522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:19.794075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:24.347719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:28.874935image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:33.546075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:38.035216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:42.846988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:34.465570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:39.240055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:43.937986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:48.694603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:53.216117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:57.719169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:02.123243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:06.552663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:11.336893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:15.712628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:20.074650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:24.628893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:29.160792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:33.826406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:38.310699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:43.146671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:34.746797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:39.546656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:44.222044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:48.962774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:53.489709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:57.992393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:02.390401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:06.843750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:11.605804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:15.965948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:20.350743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:24.905579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:29.429183image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:34.100922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:38.579940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:43.453918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:35.037074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:39.856877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:44.502375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:49.256175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:53.777496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:58.263166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:02.659460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:07.178427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:11.875339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:16.228826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:20.619748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:25.189868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:29.709566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:34.380876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:38.856717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:43.755205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:35.323752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:40.175421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:44.797331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:49.545222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:54.068771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:58.533859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:02.932804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:07.466256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:12.153032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:16.488895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:20.895976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:25.467416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:29.995307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:34.659322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:39.136415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:44.065575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:35.632405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:40.487238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:45.097030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:49.836134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:54.361737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:58.805107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:03.202445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:07.748219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:12.432792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:16.757658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:21.185074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:25.748821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:30.270874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:34.940683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:39.420588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:44.364175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:35.929074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:40.807042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:45.377899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:50.126348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:54.644018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:59.074197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:03.471307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:08.027570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:12.709847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:17.022490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:21.468674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:26.040548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:30.549556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:35.215306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:39.698320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:44.671081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:36.234133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:41.122814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:45.656959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:50.412983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:54.934068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:59.350650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:03.733917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:08.307855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:12.987708image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:17.285667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:21.758031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:26.326880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:30.833847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:35.502555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:39.969572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:44.959418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:36.524032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:41.430771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:45.931908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:50.692114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:55.218904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:15:59.613915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:03.996521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:08.625343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:13.263678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:17.549208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:22.039386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:26.607695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:31.113607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:35.783312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-18T16:16:40.241600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-18T16:16:52.192544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
canaldensidad_habdescr_flag_patentedolarid_barrioid_clienteid_comunaid_periodoimacecindice_gseipcliq_umn_habitantesn_ptos_interessegmentosuperficie_km2tasa_desempleotpmuf
canal1.0000.0480.1310.0480.0730.0590.0710.0670.0790.2200.0300.1200.1000.2171.0000.0270.0900.0670.083
densidad_hab0.0481.0000.029-0.0170.261-0.0690.261-0.0160.0260.4590.0090.0860.4190.1590.118-0.894-0.009-0.016-0.016
descr_flag_patente0.1310.0291.0000.0590.0750.0620.0740.0940.0990.0860.0420.0030.0550.0420.3690.0090.0650.0680.095
dolar0.048-0.0170.0591.000-0.0080.001-0.0080.873-0.062-0.0010.191-0.015-0.0020.0040.0460.0190.2450.5330.874
id_barrio0.0730.2610.075-0.0081.000-0.1130.996-0.0060.0250.0840.0120.0540.1120.1340.168-0.1790.005-0.018-0.007
id_cliente0.059-0.0690.0620.001-0.1131.000-0.111-0.005-0.016-0.055-0.0150.002-0.024-0.0340.0730.052-0.010-0.001-0.004
id_comuna0.0710.2610.074-0.0080.996-0.1111.000-0.0050.0250.0830.0130.0530.1100.1330.167-0.1790.006-0.017-0.006
id_periodo0.067-0.0160.0940.873-0.006-0.005-0.0051.000-0.0050.0040.122-0.0200.0020.0070.0970.0200.4240.5991.000
imacec0.0790.0260.099-0.0620.025-0.0160.025-0.0051.0000.0380.255-0.0500.0180.0250.098-0.020-0.230-0.151-0.005
indice_gse0.2200.4590.086-0.0010.084-0.0550.0830.0040.0381.0000.018-0.0670.2780.4730.144-0.430-0.0200.0060.004
ipc0.0300.0090.0420.1910.012-0.0150.0130.1220.2550.0181.000-0.0010.0090.0130.045-0.004-0.0720.2200.114
liq_um0.1200.0860.003-0.0150.0540.0020.053-0.020-0.050-0.067-0.0011.0000.127-0.0890.057-0.0410.039-0.030-0.023
n_habitantes0.1000.4190.055-0.0020.112-0.0240.1100.0020.0180.2780.0090.1271.0000.2370.101-0.0450.021-0.0170.001
n_ptos_interes0.2170.1590.0420.0040.134-0.0340.1330.0070.0250.4730.013-0.0890.2371.0000.116-0.170-0.0270.0180.007
segmento1.0000.1180.3690.0460.1680.0730.1670.0970.0980.1440.0450.0570.1010.1161.0000.0230.0620.0580.091
superficie_km20.027-0.8940.0090.019-0.1790.052-0.1790.020-0.020-0.430-0.004-0.041-0.045-0.1700.0231.0000.0240.0080.020
tasa_desempleo0.090-0.0090.0650.2450.005-0.0100.0060.424-0.230-0.020-0.0720.0390.021-0.0270.0620.0241.000-0.1320.423
tpm0.067-0.0160.0680.533-0.018-0.001-0.0170.599-0.1510.0060.220-0.030-0.0170.0180.0580.008-0.1321.0000.599
uf0.083-0.0160.0950.874-0.007-0.004-0.0061.000-0.0050.0040.114-0.0230.0010.0070.0910.0200.4230.5991.000

Missing values

2025-10-18T16:16:45.219130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-18T16:16:46.495737image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-18T16:16:49.054927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

id_clienteid_periodoliq_umid_barrioid_comunasegmentocanaldescr_flag_patenteindice_gsen_habitantesn_ptos_interessuperficie_km2densidad_habufdolaripcimacectpmtasa_desempleo
01320210118.98400230114.02301.0MAMAYORISTACON PATENTE0.0888003547.03.00.7891934494.46414129123.74741.400.7-3.10.5010.23
1132021029.89520230114.02301.0MAMAYORISTACON PATENTE0.0888003547.03.00.7891934494.46414129287.38708.040.2-2.20.5010.30
2132021038.95776230114.02301.0MAMAYORISTACON PATENTE0.0888003547.03.00.7891934494.46414129394.77732.110.46.40.5010.35
31320210421.31920230114.02301.0MAMAYORISTACON PATENTE0.0888003547.03.00.7891934494.46414129494.13705.090.414.10.5010.24
4132021057.89600230114.02301.0MAMAYORISTACON PATENTE0.0888003547.03.00.7891934494.46414129613.26724.920.318.10.5010.04
5132021067.93800230114.02301.0MAMAYORISTACON PATENTE0.0888003547.03.00.7891934494.46414129709.83735.280.120.10.509.50
6132022054.43520230114.02301.0MAMAYORISTACON PATENTE0.0888003547.03.00.7891934494.46414132679.54826.261.26.48.257.80
7132022060.00000230114.02301.0MAMAYORISTACON PATENTE0.0888003547.03.00.7891934494.46414133086.83919.970.93.79.007.81
8132022080.11088230114.02301.0MAMAYORISTACON PATENTE0.0888003547.03.00.7891934494.46414133836.51882.111.20.09.757.93
9332021031.23648710120.07101.0BACONSUMOCON PATENTE0.08169815800.07.02.4224586522.30059929394.77732.110.46.40.5010.35
id_clienteid_periodoliq_umid_barrioid_comunasegmentocanaldescr_flag_patenteindice_gsen_habitantesn_ptos_interessuperficie_km2densidad_habufdolaripcimacectpmtasa_desempleo
227514272751320200311.371921310133.013101.0BOCOMPRACON PATENTE0.16563415080.035.00.67097722474.67934428597.46846.300.3-3.51.008.23
227514372751320200410.670521310133.013101.0BOCOMPRACON PATENTE0.16563415080.035.00.67097722474.67934428690.73836.620.0-14.10.509.00
227514472751320200512.684001310133.013101.0BOCOMPRACON PATENTE0.16563415080.035.00.67097722474.67934428716.52812.74-0.1-15.30.5011.21
227514572751320200617.949121310133.013101.0BOCOMPRACON PATENTE0.16563415080.035.00.67097722474.67934428696.42816.36-0.1-12.40.5012.25
227514672751320200715.227101310133.013101.0BOCOMPRACON PATENTE0.16563415080.035.00.67097722474.67934428667.44754.450.1-10.70.5013.09
227514772751320200813.796161310133.013101.0BOCOMPRACON PATENTE0.16563415080.035.00.67097722474.67934428679.45779.920.1-11.30.5012.93
227514872751320200916.771441310133.013101.0BOCOMPRACON PATENTE0.16563415080.035.00.67097722474.67934428707.85784.460.6-5.30.5012.35
22751497275152019120.05880830410.08304.0APCONSUMOSIN PATENTE0.04612811809.029.026.167931451.27755228309.94744.620.11.11.756.98
22751507275162024100.00000NaN13132.0IECONSUMONaNNaNNaNNaNNaNNaN37971.42950.891.02.35.258.58
22751517275172023120.000001350313.013503.0APCONSUMOSIN PATENTE0.0688591590.02.062.10264325.60277536789.36884.59-0.5-1.08.258.48